Generative Artificial Intelligence (GenAI) is an advanced technology that can change not only business processes, but also the world around us. For the full implementation of GenAI, it is necessary to develop a set of generally accepted methods and practices regarding the technology – AI management.
Implementation challenges
The benefits of generative AI solutions for business, despite their relative novelty, are already clear. Working with the help of “smart” tools of the new generation will be easier, faster and more efficient than the usual level of digitalization.
However, you have to pay for everything. Adapting to GenAI requires a lot of organizational and management efforts. The challenges are in many ways similar to the recent landscape of big data and analytics.
Let’s remember: just yesterday, one of the main tasks of digital transformation was the control of the life cycle of data – from collection and storage to receiving the request, sending, processing, analyzing and interpreting the results. which was obtained. All these tasks are combined with the concept of Data Governance as the sum of policies, procedures and technologies used to work with data. As a general approach, this should be pursued in relation to AI – in AI Governance.
One of the strategic challenges of AI Governance today is to ensure the consistency and reliability of decisions made by machine intelligence and the formation of general trust in the results of its work.
Another big problem is ensuring the confidentiality and security of the data. Leaks and controversial use cases for AI work can undermine confidence in the next generation of smart tools.
And this is the top part of the pyramid.
Why now
If you don’t start building AI Governance now, then it will become more difficult, as AI penetrates into more processes and areas of activity, and as the complexity of AI itself grows. The current stage of development of artificial intelligence is hope at the beginning of the road from highly specialized (“weak”) to more general AI (“average”).
“Weak” AI is aimed at performing specific, highly specialized tasks for which it has been previously trained. For example, identifying a specific set of images.
“Average” AI can perform intelligent tasks even if it is not trained to do so. That is, like a person, he can generalize any input, based on general knowledge, for example, that any object has weight, size, color . The move to “middle” AI will represent a significant step forward in the application capabilities of the technology.
But the evolution doesn’t end there either. In addition, it is possible to create Super AI – an even more advanced level where AI can surpass humans in many aspects. Although this is not something in the near future.
“Mid” and “super” AI has the potential to manage the operation of clusters of systems and processes throughout the industry and even at the cross-industry level, where connections exist within single value chains.
This means that AI can integrate complex ecosystems, enabling more efficient collaboration between different sectors and stages of production, leading to significant improvements in efficiency and productivity.
But the risks and consequences of various interruptions and failures in the operation, development and management of new generation devices will increase proportionally. This is why the research and development of AI Governance takes place today, to ensure that there is maximum alignment between the growth of the potential of AI and the ability to overcome the accompanying pains.
Practical aspects and recommendations
The basis for building any AI Governance approach for a business implementing AI or GenAI should include the following basic aspects:
- Employee training and adaptation
It is important to conduct training and outreach to staff to reduce fears, increase confidence, and increase awareness of the risks of intentional use. All employees should be involved in the GenAI implementation process and their opinions should be taken into account, thus reducing the level of resistance to change.
- Safety first
Developing and implementing strong data protection policies and procedures will help prevent breaches and increase confidence in the security of using AI and GenAI.
- Pilot projects and gradual integration
Adapting GenAI from small pilot projects allows you to better evaluate effectiveness and identify potential problems at an early stage.
- Testing before GenII launch
AI doesn’t always understand the broad context of a task, which can lead to mistakes and wrong decisions. The lack of adequate testing, evaluation, and validation of models exacerbates this problem. It is important to thoroughly test and evaluate AI systems before implementation to ensure their reliability and the adequacy of the decisions made.
- Determining centers of responsibility for AI decisions
The ambiguity of the question of “who is to blame when AI goes wrong” creates additional difficulties during implementation. Proving error and a direct link between an AI decision and the harm caused can be difficult. Clear accountability mechanisms should be put in place to resolve any issues.
Ethical iceberg
The future of AI—especially its manufacturing capabilities—raises many open questions. It is related not only to technical aspects, but also fundamental changes in the economy, society and even philosophy.
- The adaptation of AI to the nature of the production market
The most important question is how AI fits into the existing nature of the economic market. Traditional competition models involve human involvement at all levels, from idea to execution. With the development of AI, these processes can be changed significantly. The search for optimal methods will not be implemented as a competition of ideas, but as a precise calculation that takes into account thousands of parameters to increase efficiency and reduce costs. Which will inevitably affect the structure of the market and the distribution of its roles.
- Competition in the era of “super” AI
What will competition look like if AI can optimize entire industries and industries? Traditional competitive advantages—unique technology or a skilled workforce—may no longer matter. However, competitiveness can be determined by the ability to effectively integrate and use AI.
- Ownership model
Who will own AI systems and their results? Is it possible that new forms of ownership will emerge, such as collective or distributed ownership of AI and its products? The question of who controls the data and algorithms becomes increasingly important as technology advances.
- Center for Strategic Goal Setting
Who or what will be at the center of strategic decision-making? In traditional companies, this role is played by top managers and business owners. With the introduction of AI, it is possible that this function will move to automated systems capable of analyzing large volumes of data and proposing the best strategies.
No one leaves hurt?
The most philosophical question concerns the optimization objectives. What exactly can we optimize with AI? Different scenarios are possible:
- owner’s profit: the focus on profit maximization may remain an important factor, but it raises questions about social responsibility and sustainable development of society;
- meeting the needs of the end consumer: such a shift in focus can lead to the creation of more personalized and high-quality products and services, on the one hand, but endless consumption cannot be an end to himself;
- production and environment balance: optimization to achieve sustainable development, minimize environmental problems and harmonious interaction with nature.
Open questions about the future of AI highlight the complexity and complexity of the changes that await us. The answers will depend on many factors, including technological progress, regulatory frameworks, public values and economic conditions.
It is important that society actively participates in discussing and shaping the future of AI so that this powerful technology can benefit everyone, not just a select few.